|Publication number||US7120880 B1|
|Application number||US 09/257,208|
|Publication date||Oct 10, 2006|
|Filing date||Feb 25, 1999|
|Priority date||Feb 25, 1999|
|Publication number||09257208, 257208, US 7120880 B1, US 7120880B1, US-B1-7120880, US7120880 B1, US7120880B1|
|Inventors||D. Christopher Dryer, Myron Dale Flickner, Jianchang Mao|
|Original Assignee||International Business Machines Corporation|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (21), Non-Patent Citations (26), Referenced by (201), Classifications (5), Legal Events (5)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The present application is related to U.S. patent application Ser. No. 09/257,200, filed on Feb. 25, 1999, to Flickner et al., entitled “METHOD AND SYSTEM FOR RELEVANCE FEEDBACK THROUGH GAZE TRACKING AND TICKER INTERFACES”, assigned to the present assignee, and incorporated herein by reference.
1. Field of the Invention
The present invention relates to a method and system for determining a subject interest level to media content, and specifically to the level of interest a subject expresses in content of an image on a display. More particularly, the invention relates to a method and system for non-intrusively detecting how interested a subject is to media content (e.g., the content originating from broadcast or cable TV, the web, a computer application, a talk, a classroom lecture, a play, etc.).
2. Description of the Related Art
Information technologies have become quite efficient at data transmission. However, users are not interested in data per se, but instead want data that is useful for a particular task. More specifically, people desire interesting information suited to a particular topic, problem, etc. The importance of providing interesting information in communication has been noted by various philosophers and scientists, including Grice, H. P. Logica and Conversation, in: P. Cole & J. Morgan (Eds.), Syntax and Semantics 3: Speech Acts, pp. 41–58, (New York: Academic Press, 1967) who urged that speakers must make their communication relevant to the listener if communication is to be successful.
The problem of determining whether data is interesting to a receiver has been addressed in different ways within different media. In interpersonal communication, a listener provides a speaker with verbal and non-verbal feedback (e.g., cues) that indicates the listener's level of interest.
In many mass media, such as television, multiple channels that offer some variety of information are provided, and people receiving the information select from the available information whatever seems most interesting. Then, people's selections are measured (e.g., typically by sampling a small segment of viewers such as by the Nielsen ratings or the like), so that more interesting and new (potentially interesting) content can be made more available, and content that is not interesting can be made less available.
The interpersonal means of interest level detection has an advantage over the typical mass media means in that in the interpersonal medium, interest level detection occurs in real time, within a single exchange of information rather than between a plurality of exchanges of information. The speaker can introduce information, assess the listener's interest in the information and then consider the listener's interests when presenting subsequent information. Thus, the speaker can tailor the subsequent information depending upon the listener's perceived interest.
Mass media technologies typically rely on less immediate feedback (e.g., again through ratings or the like of a small population sample, oftentimes not proximate to the original presentation of the information). A drawback to this procedure is that people have to search through information, looking for something interesting, only to discover that sometimes none of the available information is interesting. Currently, there are no methods or systems for assessing and communicating a person's level of interest by passively observing them, especially in a mass media technology environment.
It is noted that some conventional systems and methods exist for assessing a mental state of a person, but these systems and methods have certain drawbacks.
In one conventional system, a device is provided for estimating a mental decision. This estimate is performed by monitoring a subject's gaze direction along with the subject's EEG, and by processing the output signals via a neural network to classify an event as a mental decision to select a visual cue. Thus, the device can detect when a subject has decided to look at a visual target. The EEG is detected via skin sensors placed on the head.
In a second conventional method and system, a person's emotional state is determined remotely. Such a technique is performed by broadcasting a waveform of predetermined frequency and energy at an individual, and then detecting and analyzing the emitted energy to determine physiological parameters. The physiological parameters, such as respiration, blood pressure, pulse rate, pupil size, perspiration levels, etc. are compared with reference values to provide information indicative of the person's emotional state.
In yet another conventional system, a method is provided for evaluating a subject's interest level in presentation materials by analyzing brain-generated event related potential (ERP) and/or event related field (ERF) waveforms. Random audio tones are presented to the subject followed by measurement of ERP signals. The level of interest is computed from the magnitude of the difference of a baseline ERP signal and an ERP signal during a task (e.g., during a video presentation). The difference is correlated to the interest level which the subject expressed by filling out a questionnaire about the video presentations. ERP measurement requires scalp sensors and although it has been suggested that using EMF signals would allow such a technique to be performed non-intrusively, no evidence or practical implementation is known which makes possible such non-intrusive activity.
In other work, it has been determined that perplexed behaviors of a subject using a word processor resulted in head motion changes more than facial expression changes. Dynamic programming is employed to match head motion with head motion templates of the following head gestures: nod, shake, tilt, lean backwards, lean forwards, and no movement. When the subject (user) displays appropriate head gestures, it can be detected when the person is perplexed.
However, in the above technique, only perplexed behaviors, not a general level of interest, was detected.
Other experiments have been performed which indicate that people naturally lean forward when presented positive valence information. In one experiment, a mouse with a trackpoint was used and the forward pressure on the trackpoint was measured and then correlated with the valence level of presented information.
No methods or systems exist for assessing and communicating a person's level of interest in real-time by passively observing them, especially in a mass media technology environment.
In view of the foregoing and other problems of the conventional methods and systems, an object of the present invention is to reliably assess and communicate a subject's interest level to media content and more particularly to assessing a subject's level of interest in realtime by passively observing the subject.
Another object of the present invention is to provide a non-intrusive method of detecting interest level whereas the prior art has required intrusive detection or detects only emotional information but not the level of the subject's interest in the information.
In a first aspect of the present invention, a system and method are provided for unobtrusively detecting a subject's level of interest in media content, which includes means for detecting to what a subject is attending; means for measuring a subject's relative arousal level; and means for combining arousal level and attention to produce a level of interest.
Thus, the system and method assess whether a person is attending to the target information (e.g., such as media content). For example, if the person is not attending to the information, the person is assumed to be not interested in the information at that time. Attention can be assessed in various ways depending on the particular medium. In visual media, for example, people reliably attend to the visual information to which their gaze is directed. Therefore, devices that determine at which target a person is looking, such as eye trackers or the like, can be used for attention detection in the visual media.
Furthermore, it has been shown that the duration of fixation time is a strong cue of indicated interest. People gaze at things longer when they are interested in them. It is noted that “target information” is defined as the object of attention or any object a person could attend to and a level of interest could be assessed.
Next, a person's relative arousal level is assessed. If a person is more aroused when they attend to target information, the person is assumed to find that information interesting at that time. Arousal in this case is a general affective state and can be assessed in various ways. For example, in interpersonal communication, speakers use facial expression as a means of assessing arousal and consequently interest. Therefore, devices that determine a person's arousal level, such as facial gesture detectors, can be used to assess arousal.
Finally, by combining data about attention and arousal, the method and system according to the present invention assesses the level of interest a person has in a particular information target (media content). This assessment can then be communicated as feedback about the information target (media content).
With the invention, a subject's level of interest in information presented to the subject can be reliably and unobtrusively assessed in realtime.
In another aspect of the invention, a method for detecting a person's level of interest in presented target information, includes assessing whether a person is attending to the target information, to produce first data; assessing a person's relative arousal level with regard to the target information, to produce second data; combining the first and second data to determine a level of interest the person has in the target information; and communicating the level of interest as feedback about the target information to a manager of the target information.
Finally, in yet another aspect of the invention, a signal medium is provided for storing programs for performing the above methods.
For example, in a first signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method for computer-implemented unobtrusive detection of a subject's level of interest in media content, the method includes detecting to what a subject is attending; measuring a subject's relative arousal level; and combining arousal level and attention to produce a level of interest.
In a second signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method for computer-implemented unobtrusive detection of a subject's level of interest in media content, the method includes assessing whether a person is attending to the target information, to produce first data; assessing a person's relative arousal level with regard to the target information, to produce second data; combining the first and second data to determine a level of interest the person has in the target information; and communicating the level of interest as feedback about the target information to a manager of the target information.
The foregoing and other purposes, aspects and advantages will be better understood from the following detailed description of a preferred embodiment of the invention with reference to the drawings, in which:
Referring now to the drawings, and more particularly to
First, as shown in the flow diagram of
First, in step 101, information is presented.
In step 102, the attention indicators (features) of the subject are measured.
In step 103, it is determined whether the subject is attending to target information based on the attention indicators/features measured in step 102. In determining what the subject is attending, preferably the subject's gaze is tracked. There are many methods to track gaze, and for example, many methods are described in Young et al., “Methods and Designs: Survey of Eye Movement Recording Methods”, Behavior Research Methods and Instrumentation, Vol 7, pp. 397–429, 1975. Since it is desirable to observe gaze unobtrusively, preferably a remote camera-based technique is employed such as the corneal glint technique taught in U.S. Pat. No. 4,595,990 to Garwin et al. entitled, “Eye Controlled Information Transfer” and further refined in U.S. Pat. Nos. 4,536,670 and 4,950,069 to Hutchinson.
Instead of custom-built eye/gaze trackers, commercially available systems, such as the EyeTrac® Series 4000 product by Applied Science Labs, Inc. and the EyeGaze® system by LC Technologies, Inc. can be implemented with the invention.
An improvement on the commercial systems that allows for more head motion uses a novel person detection scheme that uses optical properties of pupils, as described in “Pupil Detection and Tracking Using Multiple Light Sources”, by Morimoto et al., IBM Research Report RJ 10117, April, 1998, incorporated herein by reference, in Ebesawa et al., “Unconstrained Pupil Detection Technique Using Two Light Source and the Image Differencing Method”, Visualization and Intelligent Design Architecture, pp. 79–89, 1995, and in U.S. Pat. No. 5,016,282 issued to Tomono et al. (also published in Tomono et al., “A TV Camera System Which Extracts Feature Points For Non-Contact Eye Movement Detection”, SPIE, Vol 1194, Optics Illumination and Image Sensing for Machine Vision IV, 1989.
By finding the person by, for example, using a relatively wide field lens, the high resolution tracking camera can be targeted and avoid getting lost during large fast head and upper body motions. The output of the gaze tracker can be processed to give sets of fixations. This operation can be performed as described in Nodine et al., “Recording and Analyzing Eye-Position Data Using a Microcomputer Workstation”, Behavior Research Methods, Instruments & Computers, 24:475–485, 1992, or by purchasing commercial packages such as the EYEANAL® from Applied Science Labs, Inc. The gaze-tracking device may be built into a display to which the person is gazing or may be provided separately from the display.
The fixation locations are mapped to applications/content on a screen/television monitor or object in a 3-D environment. The durations (e.g., as measured by a timer provided either separately or built into a CPU) are used to rank the fixation to signal the strength of attention level. A longer fixation indicates a higher attention level. In a room setting, the gaze vector can be used along with a 3-D model of the room to determine what object the subject is looking at. Once it is known at which object the subject is looking, the subject's level of attention toward that object, as well as the subject's history of attention to various objets, can be determined. Additionally, it is known what target information the subject has not yet seen, and thus interest level of those targets cannot be assessed.
The next step is to measure and assess the subject's relative arousal level (e.g., step 104). Specifically, in step 104, if the subject is attending to the target information, then the subject's arousal level must be measured.
Here, for example, the technique of analyzing facial gestures from video sequences is employed. Hence, an arousal-level assessment means may be employed. For example, as described in Ekman et al., “Unmasking the Face”, Prentice-Hall: Englewood Cliffs, N.J. (1971), incorporated herein by reference, a system of coding facial expressions has been used to characterize human emotions. Using this system, human emotions such as fear, surprise, anger, happiness, sadness and disgust can be extracted by analyzing facial expressions. Computer vision researchers have recently codified the computation of these features, as described for example, in Black et al., “Recognizing Facial Expressions in Image Sequences using Local Parameterized Models of Image Motion”, International Journal of Computer Vision, 25 (1) (1), pp. 23–48, 1997, C. Lisetti et al., “An Environment to Acknowledge the Interface Between Affect and Cognition”, AAAI, Tech report SS-98-2, pages 78-86, 1998, J. Lien et al., “Automated Facial Expression Recognition based on FACS Action Units”, Proceeding of the FG '98, IEEE, April 1998, Nara Japan, J. Lien et al., “Automatically Recognizing Facial Expression in the Spatio-Temporal Domain”, Workshop on the Perceptual User Interfaces, pp 94-97 Banaff, Canada, October 1997, J. Lien et al., “Subtly Different Facial Expression Recognition and Expression Intensity Estimations”, Proceedings of CVPR '98, IEEE, Santa Barbara, June 1998, and I. Essa et al., “A Vision System For Observing and Extracting Facial Action Parameters”, Proceedings of CVPR '94, IEEE, pp 76–83, 1994, all of which are incorporated herein by reference.
Additionally, as another or alternative arousal-level assessment mechanism, by observing head gestures such as approval/disapproval, nods, yawns, blink rate/duration, and pupil size and audio utterances, a measure of the arousal level of the subject at the current time can be obtained. For example, decreasing blink rate and increasing blink duration is a strong indicator that the subjects is falling asleep, and thus has a low arousal level. This type of detection has been used to detect the onset of sleep in drivers of cars, as described in M. Eriksson et al., “Eye Tracking for Detection of Driver Fatigue”, IEEE Conference on Intelligent Transportation Systems, 1997, pp. 314–319, and M. Funada et al., “On an Image Processing of Eye Blinking to Monitor Awakening Levels of Human Beings”, Proceedings of IEEE 18th International Conference in Medicine and Biology, Vol. 3, pp. 966-967, 1996, incorporated herein by reference, and U.S. Pat. No. 5,786,765 to Kumakura et al., incorporated herein by reference. In contrast, multiple approval nods are a strong indication that the subjects are alert and interested.
It is noted that, in the exemplary implementation, speech is not integrated, for brevity and ease of explanation. However, it is noted that speech content and vocal prosody can be used to help decide a person's affective station. Expression like “yeah”, “right” etc. indicate strong interest, whereas expressions like “blah”, “yuck” etc. indicate strong disinterest. As noted in R. Banse et al., “Acoustic Profiles in Vocal Emotion Expression”, Journal of Personality and Social Psychology, 70, 614-636, (1997), vocal characteristics, such as pitch, can indicated levels of arousal. Such speech content and vocal prosody could be integrated into the arousal assessment means according to the present invention, either additionally or alternatively to the arousal assessment mechanisms discussed above.
Blink rate can be measured by simply analyzing the output of the pupil detection scheme, as described in C. Morimoto et al., “Pupil Detection and Tracking Using Multiple Light Sources”, IBM Research Report RJ 10117, April, 1998. Whenever both pupils disappear, a blink is marked and the duration is measured. The blink rate is computed by simply counting the last few blinks over a period of time and dividing by the time. A decreasing blink rate and increasing blink duration is a strong indicator that the subject is falling asleep and thus has a low arousal level.
Upper body motion can be detected by analyzing the motion track of the pupil over time. To extract this information, as taught by T. Kamitaini et al., “Analysis of Perplexing Situations in Word Processor Work Using Facial Image Sequence”, Human Vision and Electronic Imaging II, SPIE vol 3016, 1997 pp. 324-334. The present invention computes x, y, z and tilt angle of the head by simple analysis of the pupils' centers. The motion in x and y is computed using a finite difference of the left and right pupil center averages. A motions in the z axis can be obtained using finite differences on the measured distance between the pupils. The tilt angle motion can be computed using finite differences on the angle between the line connecting the pupils and a horizontal line.
Then, a distance between the gesture is computed using dynamic programming to the following templates: yes nod, no nod, lean forward, lean backward, tilt and no action. The output of this stage are 6 distances to the 6 gestures. These distances is computed over the previous 2 seconds worth of data and updated each frame.
To extract information from facial gestures, the eyebrow and mouth region of the person's face are examined. The pupil finding technique indicates a location of the pupils of a person. From this information and a simple face model, regions of the eyebrows and the region of the lips are extracted. For example, pitch may indicate “yes”, a yaw motion may indicate “no”, and a roll may indicate “I don't know”.
To identify the eyebrows, two rectangular regions are extracted using the line connecting the two pupils, as shown in
To allow for invariance to up and down rotation (e.g., a “yes” gesture movement), the ratio of the distances are computed. The muscles of the face only act on the medial point. The temporal point remains fixed on the head, but the distance will change due to perspective from up/down head rotation. The ratio of the distances reflects changes due to the medial point from face muscles and not head motion.
To identify the mouth, the mouth is found again by using the coordinate system aligned to the lines between the pupils. Here, a corner of the mouth is found. This is done by searching for corners using a corner detection scheme. Here, the eigenvalues of the windowed second moment matrix is found, as outlined on pages 334–338 of R. Haralick, “Computer and Robot Vision”, Vol. 2, Addison Wesley, 1993), incorporated herein by reference. Then the perpendicular distance between the mouth corner and the baseline between the pupils is computed. This distance indicates the extent to which the subject is smiling (e.g., as in an expression of happiness) or frowning (e.g., as in an expression of sadness). This expression occurs through the action of the zygomatic muscle.
In summary, the features extracted are as follows: what the subject is looking at, the subject's blink rate and blink duration, six distances to six head gestures, the relative position of his eyebrows, and the relative position of the corners of his mouth.
The next step (e.g., step 105) is to infer the subject's interest level from these features (or measurements). The preferred method for this purpose is a Bayesian network which is sometimes called a “belief network”. Other machine learning techniques, such as decision trees and neural networks can also be used. However, Bayesian networks offer several advantages in handling missing data (features), learning and explaining causal relationship between various attributes including features, incorporating expert knowledge, and avoiding over-fitting of data.
A Bayesian network is an acyclic-directed graph (without any loops) in which nodes represent variables and arcs represent cause-effect relationship (e.g., an arc from node a to b indicates that variable a is a direct cause for variable b). Each node is associated with a conditional probability distribution P(xi|IIi), where IIi denotes the parents of the node variable xi. The strength of the causal relationship is encoded in this distribution. A beneficial property of Bayesian networks is that the joint probability distribution encoded in the network can be computed by the product of all the conditional probability distributions stored in its nodes. If a node has no parents, then the conditional variable is empty.
Once a Bayesian network is built, one can issue a number of queries. For example, given a set of observations (e.g., often-called “evidence”) on the states of some variables in the network, one can infer the most probable state(s) for any unobserved variable(s). This applies to the problem of inferring a subject's interest level given the observations on subject's gaze fixation density, blink rate and duration, head movement, body movement, and facial expression (e.g., eyebrows distance and mouth distance). It is noted that the fixation density is the number of fixation per unit time (seconds) per window. A “window” is a fixed portion of a display screen (e.g., typically rectangular or square), but which typically has separate controls for sizing and the like. A typical window may have a 2-inch by 2-inch dimension, or the like. It is noted that it is unnecessary to have all the features in order to infer the subject's interest level. This is particularly desirable because some features may not be reliably obtained under certain circumstances.
The structure and parameters of a Bayesian network can be learned from experimental data using the algorithms described in D. Heckerman, “A Tutorial on Learning with Bayesian Network”, MSR-TR-95-06, and E. Castillo et al., “Expert Systems and Probabilistic Network Models”, Springer, 1998. Bayesian networks have been used for performing collaborative filtering (e.g., see U.S. Pat. No. 5,704,017, incorporated herein by reference), and probabilistic subject modeling based on a subject's background, actions, and queries (e.g., see E. Horvitz et al., “The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users”, Proc. of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, Wis. July, 1998).
One use of this system is for an information presentation (media content) technology to receive interest level data about various information targets, and then present more information that is similar to the targets that were most interesting and present less information that is similar to the targets that were least interesting. It is noted that the present invention may utilize other classification schemes instead of the above-described scheme.
As shown in
Such a method may be implemented, for example, by operating the CPU 501 (
Thus, this aspect of the present invention is directed to a programmed product, comprising signal-bearing media tangibly embodying a program of machine-readable instructions executable by a digital data processor incorporating the CPU 501 and hardware above, to perform a method of determining a person's interest to media content.
This signal-bearing media may include, for example, a RAM (not shown) contained within the CPU 501, as represented by the fast-access storage for example. Alternatively, the instructions may be contained in another signal-bearing media, such as a magnetic data storage diskette 600 (
Whether contained in the diskette 600, the computer/CPU 501, or elsewhere, the instructions may be stored on a variety of machine-readable data storage media, such as DASD storage (e.g., a conventional “hard drive” or a RAID array)! magnetic tape, electronic read-only memory (e.g., ROM, EPROM, or EEPROM), an optical storage device (e.g. CD-ROM, WORM, DVD, digital optical tape, etc.), paper “punch” cards, or other suitable signal-bearing media including transmission media such as digital and analog and communication links and wireless. In an illustrative embodiment of the invention, the machine-readable instructions may comprise software object code, compiled from a language such as “C”, etc.
With the massive amount of digital information, all Internet-based information systems face the challenge of providing the subjects with quality information that is relevant to their individual personal interests. Hence, most existing systems demand (or at least strongly request) that subjects provide an explicit interest profile or explicit vote on individual web pages. Such activities put significant burdens on subjects, who want merely to get the best information with the least trouble in the quickest possible manner.
By integrating gaze-tracking with an arousal-level assessment mechanism and an information source (e.g., a display such as a ticker display), the system according to the present invention can automatically collect valuable feedback passively, without requiring the subject to take any explicit action such as completing a survey form, undergoing a registration process, or the like.
Using the same techniques described previously for determining whether to display more relevant information to a subject, the system generates relevance feedback based on whether the subject is paying attention to certain display items. Accordingly, the system “learns” the subject's particular interests, and the system adaptively provides information regarding such interests to the subject.
A key advantage of this approach is that the system may have different levels of confidence in the subject's interests in a certain topic because it provides different levels of details for any display item. Thus, the system is adaptive to the subject's interests, and stores information broadly representing the subject's interests in a database or the like. Similarly, negative feedback can also be noted in the subject's profile, and, eventually the subject's display will display mainly items of information in which the subject has a high interest.
While the invention has been described in terms of a preferred embodiment, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the appended claims.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US4950069||Nov 4, 1988||Aug 21, 1990||University Of Virginia||Eye movement detector with improved calibration and speed|
|US5802220 *||Dec 15, 1995||Sep 1, 1998||Xerox Corporation||Apparatus and method for tracking facial motion through a sequence of images|
|US5825355||Jan 27, 1993||Oct 20, 1998||Apple Computer, Inc.||Method and apparatus for providing a help based window system using multiple access methods|
|US5886683||Jun 25, 1996||Mar 23, 1999||Sun Microsystems, Inc.||Method and apparatus for eyetrack-driven information retrieval|
|US5898423||Jun 25, 1996||Apr 27, 1999||Sun Microsystems, Inc.||Method and apparatus for eyetrack-driven captioning|
|US5920477||Jun 6, 1995||Jul 6, 1999||Hoffberg; Steven M.||Human factored interface incorporating adaptive pattern recognition based controller apparatus|
|US5959621||Dec 6, 1996||Sep 28, 1999||Microsoft Corporation||System and method for displaying data items in a ticker display pane on a client computer|
|US5983129 *||Feb 19, 1998||Nov 9, 1999||Cowan; Jonathan D.||Method for determining an individual's intensity of focused attention and integrating same into computer program|
|US5987415 *||Jun 30, 1998||Nov 16, 1999||Microsoft Corporation||Modeling a user's emotion and personality in a computer user interface|
|US6056781 *||Feb 6, 1998||May 2, 2000||The Dow Chemical Company||Model predictive controller|
|US6067565||Jan 15, 1998||May 23, 2000||Microsoft Corporation||Technique for prefetching a web page of potential future interest in lieu of continuing a current information download|
|US6134644||Jan 14, 1998||Oct 17, 2000||Fujitsu Limited||Method and apparatus for reproducing operation guidance information, and method and apparatus for reproducing multi-media information|
|US6182098||Jul 22, 1998||Jan 30, 2001||International Business Machines Corporation||Next/current/last ticker graphical presentation method|
|US6185534 *||Mar 23, 1998||Feb 6, 2001||Microsoft Corporation||Modeling emotion and personality in a computer user interface|
|US6195651 *||Nov 19, 1998||Feb 27, 2001||Andersen Consulting Properties Bv||System, method and article of manufacture for a tuned user application experience|
|US6212502 *||Jun 30, 1998||Apr 3, 2001||Microsoft Corporation||Modeling and projecting emotion and personality from a computer user interface|
|US6349290 *||Jun 30, 1999||Feb 19, 2002||Citibank, N.A.||Automated system and method for customized and personalized presentation of products and services of a financial institution|
|US6437758 *||Jun 25, 1996||Aug 20, 2002||Sun Microsystems, Inc.||Method and apparatus for eyetrack—mediated downloading|
|US6577329 *||Feb 25, 1999||Jun 10, 2003||International Business Machines Corporation||Method and system for relevance feedback through gaze tracking and ticker interfaces|
|US20020182574 *||Jun 7, 2002||Dec 5, 2002||Freer Peter A.||Electroencephalograph Based Biofeedback System For Improving Learning Skills|
|US20030037041 *||Oct 1, 2002||Feb 20, 2003||Pinpoint Incorporated||System for automatic determination of customized prices and promotions|
|1||Black, et al., "Recognizing Facial Expressions in Image Sequences using Local Parameterized Models of Image Motion", pp. 1-35, Mar. 1995.|
|2||Ebisawa, et al, "Examination of Eye-Gaze Detection Technique Using Two Light Sources and the Image Difference Method", SICE '94, Jul. 26-28, 1994.|
|3||Ebisawa, Y., "Improved Video-Based Eye-Gaze Detection Method", IMTC '94 May 10-12, 1994.|
|4||Erik D. Reichle, Alexander Pollatsek, Donald L. Fisher, and Keith Rayner, University of Massachusetts at Amherst, "Toward a Model of Eye Movement Control in Reading", Psychological Review 1998, vol. 105, No. 1, pp. 125-157.|
|5||Eriksson, M., "Eye-Tracking for Detection of Driver Fatigue", IEEE Conference on Intelligent Transportation Systems, Nov. 9-12, 1997, pp. 314-319.|
|6||Fernandez, et al., "Singal Processing for Recognition of Human Frustration", MIT Media Laboratory Perceptual Computing Section Technical Report No. 447, ICASSP '98, Seatlle, Washington, May 12-15, 1998, pp. 1-4.|
|7||Funada, et al., "On an Image Processing of Eye Blinking to Monitor Awakening Levels of Human Beings", IEEE Conference, pp. 966-967.|
|8||H. Rex Hartson and Deborah Hix, "Advances in Human-Computer Interaction", vol. 4, Virginia Polytechnic Institute and State University, ABLEX Publishing Corporation, Norwood, New Jersey, pp. 151-190, May 1993.|
|9||Johnmarshall Reeve and Glen Nix, "Expressing Intrinsic Motivation Through Acts of Exploration and Facial Displays of Interest", Motivation and Emotion, vol. 21, No. 3, 1997, pp. 237-250, 8 Pages Total.|
|10||Johnmarshall Reeve, "The Face of Interest", Motivation and Emotion, vol. 17, No. 4, 1993, pp. 353-375, 12 Pages Total.|
|11||Kamitani, et al., "Analysis of perplex situations in word processor work using facial image sequence", SPIE vol. 3016, pp. 324-334.|
|12||Kumakura, S., "Apparatus for estimating the drowsiness level of a vehicle driver", Jul. 28, 1998, pp. 1-2.|
|13||Lien, et al, "Automated Facial Expression Recognition Based on FACS Action Units", April 14-16, 1998, IEEE, Published in the Proceedings of FG '98, Nara, Japan.|
|14||Lien, et al, "Automatically Recognizing Facial Expressions in the Spatio-Temporal Domain", Oct. 19-21, 1997, Workshop on Perceptual User Interfaces, pp. 94-97, Banff, Alberta, Canada.|
|15||Lien, et al., "Subtly Different Facial Expression Recognition And Expression Intensity Estimation", Jun. 1998, IEEE, Published in the Proceedings of CVPR'98, Santa Barbara, CA.|
|16||Lisetti, et al, "An Environment to Acknowledge the Interface between Affect and Cognition", pp. 78-86.|
|17||Malcolm Slaney and Gerald McRoberts, "Baby Ears: A Recognition System for Affective Vocalizations", Proceedings of the 1998 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Seattle, WA, May 12-15, 1998, pp. 1-4.|
|18||Morimoto, et al., "Pupil Detection and Tracking Using Multiple Light Sources".|
|19||Morimoto, et al., "Recognition of Head Gestures Using Hidden Markov Models".|
|20||Ohtani, et al., "Eye-Gaze Detection Based on the Pupil Detection Technique Using Two Light Sources and the Image Difference Method", Sep. 20-23, 1995, pp. 1623-1624.|
|21||Pantic, et al., "Automated Facial Expression analysis", pp. 194-200.|
|22||Pantic, et al., "Automation of Non-Verbal Communication of Facial Expressions", pp. 86-93.|
|23||Paul P. Maglio, Rob Barrett, Christopher S. Campbell, Ted Selker, IBM Almaden Research Center, San Jose, CA., "SUITOR: An Attentive Information System", IUI2000: The International Conference on Intelligent User Interfaces, pp. 1-8.|
|24||Robert J.K. Jacob, Human-Computer Interaction Lab, Naval Research Laboratory, Washington, D.C., "What You Look at is What You Get: Eye Movement-Based Interaction Techniques", CHI '90 Proceedings, Apr. 1990, pp. 11-18.|
|25||Tomono, et al., "A TV Camera System Which Extracts Feature Points for Non-Contact Eye Movement Detection", SPIE vol. 1194 Optics, Illumination, and Image Sensing for Machine Vision IV (1989), pp. 2-12.|
|26||Y. Ebisawa, "Unconstrained pupil detection technique using two light sources and the image difference method", Visualization and Intelligent Design in Engineering, pp. 79-89.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US7327505 *||Feb 19, 2002||Feb 5, 2008||Eastman Kodak Company||Method for providing affective information in an imaging system|
|US7515054 *||Apr 1, 2005||Apr 7, 2009||Torch William C||Biosensors, communicators, and controllers monitoring eye movement and methods for using them|
|US7768528 *||Nov 3, 2006||Aug 3, 2010||Image Metrics Limited||Replacement of faces in existing video|
|US7889073 *||Jan 31, 2008||Feb 15, 2011||Sony Computer Entertainment America Llc||Laugh detector and system and method for tracking an emotional response to a media presentation|
|US7930199 *||Jul 21, 2006||Apr 19, 2011||Sensory Logic, Inc.||Method and report assessing consumer reaction to a stimulus by matching eye position with facial coding|
|US8054964||Apr 30, 2009||Nov 8, 2011||Avaya Inc.||System and method for detecting emotions at different steps in a communication|
|US8096660||Jul 26, 2010||Jan 17, 2012||Queen's University At Kingston||Method and apparatus for communication between humans and devices|
|US8108800 *||Jul 16, 2007||Jan 31, 2012||Yahoo! Inc.||Calculating cognitive efficiency score for navigational interfaces based on eye tracking data|
|US8136944||Aug 17, 2009||Mar 20, 2012||iMotions - Eye Tracking A/S||System and method for identifying the existence and position of text in visual media content and for determining a subjects interactions with the text|
|US8209224||Oct 29, 2009||Jun 26, 2012||The Nielsen Company (Us), Llc||Intracluster content management using neuro-response priming data|
|US8219438 *||Jun 30, 2008||Jul 10, 2012||Videomining Corporation||Method and system for measuring shopper response to products based on behavior and facial expression|
|US8235725 *||Feb 20, 2005||Aug 7, 2012||Sensory Logic, Inc.||Computerized method of assessing consumer reaction to a business stimulus employing facial coding|
|US8270814||Jan 21, 2009||Sep 18, 2012||The Nielsen Company (Us), Llc||Methods and apparatus for providing video with embedded media|
|US8292433||Sep 19, 2005||Oct 23, 2012||Queen's University At Kingston||Method and apparatus for communication between humans and devices|
|US8322856||Jun 27, 2012||Dec 4, 2012||Queen's University At Kingston||Method and apparatus for communication between humans and devices|
|US8326002||Aug 13, 2010||Dec 4, 2012||Sensory Logic, Inc.||Methods of facial coding scoring for optimally identifying consumers' responses to arrive at effective, incisive, actionable conclusions|
|US8327395||Oct 2, 2008||Dec 4, 2012||The Nielsen Company (Us), Llc||System providing actionable insights based on physiological responses from viewers of media|
|US8332883||Oct 2, 2008||Dec 11, 2012||The Nielsen Company (Us), Llc||Providing actionable insights based on physiological responses from viewers of media|
|US8335715||Nov 19, 2009||Dec 18, 2012||The Nielsen Company (Us), Llc.||Advertisement exchange using neuro-response data|
|US8335716||Nov 19, 2009||Dec 18, 2012||The Nielsen Company (Us), Llc.||Multimedia advertisement exchange|
|US8392250||Aug 9, 2010||Mar 5, 2013||The Nielsen Company (Us), Llc||Neuro-response evaluated stimulus in virtual reality environments|
|US8392251||Aug 9, 2010||Mar 5, 2013||The Nielsen Company (Us), Llc||Location aware presentation of stimulus material|
|US8392254||Aug 27, 2008||Mar 5, 2013||The Nielsen Company (Us), Llc||Consumer experience assessment system|
|US8396744||Aug 25, 2010||Mar 12, 2013||The Nielsen Company (Us), Llc||Effective virtual reality environments for presentation of marketing materials|
|US8401248||Dec 30, 2008||Mar 19, 2013||Videomining Corporation||Method and system for measuring emotional and attentional response to dynamic digital media content|
|US8421885 *||Apr 19, 2010||Apr 16, 2013||Fujifilm Corporation||Image processing system, image processing method, and computer readable medium|
|US8462996||May 19, 2008||Jun 11, 2013||Videomining Corporation||Method and system for measuring human response to visual stimulus based on changes in facial expression|
|US8464288||Jan 21, 2009||Jun 11, 2013||The Nielsen Company (Us), Llc||Methods and apparatus for providing personalized media in video|
|US8469713 *||Jul 12, 2007||Jun 25, 2013||Medical Cyberworlds, Inc.||Computerized medical training system|
|US8473345||Mar 26, 2008||Jun 25, 2013||The Nielsen Company (Us), Llc||Protocol generator and presenter device for analysis of marketing and entertainment effectiveness|
|US8494610||Sep 19, 2008||Jul 23, 2013||The Nielsen Company (Us), Llc||Analysis of marketing and entertainment effectiveness using magnetoencephalography|
|US8510166 *||May 11, 2011||Aug 13, 2013||Google Inc.||Gaze tracking system|
|US8533042||Jul 30, 2008||Sep 10, 2013||The Nielsen Company (Us), Llc||Neuro-response stimulus and stimulus attribute resonance estimator|
|US8548852||Aug 8, 2012||Oct 1, 2013||The Nielsen Company (Us), Llc||Effective virtual reality environments for presentation of marketing materials|
|US8600100||Apr 16, 2010||Dec 3, 2013||Sensory Logic, Inc.||Method of assessing people's self-presentation and actions to evaluate personality type, behavioral tendencies, credibility, motivations and other insights through facial muscle activity and expressions|
|US8602791 *||Nov 6, 2006||Dec 10, 2013||Eye Tracking, Inc.||Generation of test stimuli in visual media|
|US8620113||Apr 25, 2011||Dec 31, 2013||Microsoft Corporation||Laser diode modes|
|US8634701 *||Dec 3, 2010||Jan 21, 2014||Lg Electronics Inc.||Digital data reproducing apparatus and corresponding method for reproducing content based on user characteristics|
|US8635105||Aug 27, 2008||Jan 21, 2014||The Nielsen Company (Us), Llc||Consumer experience portrayal effectiveness assessment system|
|US8635637||Dec 2, 2011||Jan 21, 2014||Microsoft Corporation||User interface presenting an animated avatar performing a media reaction|
|US8641616 *||Oct 19, 2005||Feb 4, 2014||Sony Corporation||Method and apparatus for processing bio-information|
|US8655428||May 12, 2010||Feb 18, 2014||The Nielsen Company (Us), Llc||Neuro-response data synchronization|
|US8655437||Aug 21, 2009||Feb 18, 2014||The Nielsen Company (Us), Llc||Analysis of the mirror neuron system for evaluation of stimulus|
|US8672482||Apr 19, 2013||Mar 18, 2014||Queen's University At Kingston||Method and apparatus for communication between humans and devices|
|US8760395||May 31, 2011||Jun 24, 2014||Microsoft Corporation||Gesture recognition techniques|
|US8762202||Apr 11, 2012||Jun 24, 2014||The Nielson Company (Us), Llc||Intracluster content management using neuro-response priming data|
|US8775252 *||May 4, 2007||Jul 8, 2014||National Ict Australia Limited||Electronic media system|
|US8775975||Aug 11, 2011||Jul 8, 2014||Buckyball Mobile, Inc.||Expectation assisted text messaging|
|US8814357||Mar 19, 2012||Aug 26, 2014||Imotions A/S||System and method for identifying the existence and position of text in visual media content and for determining a subject's interactions with the text|
|US8860787||May 11, 2011||Oct 14, 2014||Google Inc.||Method and apparatus for telepresence sharing|
|US8862764||Mar 16, 2012||Oct 14, 2014||Google Inc.||Method and Apparatus for providing Media Information to Mobile Devices|
|US8863619 *||Jun 25, 2011||Oct 21, 2014||Ari M. Frank||Methods for training saturation-compensating predictors of affective response to stimuli|
|US8885877||May 20, 2011||Nov 11, 2014||Eyefluence, Inc.||Systems and methods for identifying gaze tracking scene reference locations|
|US8886581 *||Jun 25, 2011||Nov 11, 2014||Ari M. Frank||Affective response predictor for a stream of stimuli|
|US8890946||Mar 1, 2010||Nov 18, 2014||Eyefluence, Inc.||Systems and methods for spatially controlled scene illumination|
|US8898687||Apr 4, 2012||Nov 25, 2014||Microsoft Corporation||Controlling a media program based on a media reaction|
|US8903176||Nov 14, 2012||Dec 2, 2014||Sensory Logic, Inc.||Systems and methods using observed emotional data|
|US8911087||May 20, 2011||Dec 16, 2014||Eyefluence, Inc.||Systems and methods for measuring reactions of head, eyes, eyelids and pupils|
|US8918344 *||Jun 25, 2011||Dec 23, 2014||Ari M. Frank||Habituation-compensated library of affective response|
|US8929589||Nov 7, 2011||Jan 6, 2015||Eyefluence, Inc.||Systems and methods for high-resolution gaze tracking|
|US8929616||Dec 3, 2012||Jan 6, 2015||Sensory Logic, Inc.||Facial coding for emotional interaction analysis|
|US8938403 *||Jun 25, 2011||Jan 20, 2015||Ari M. Frank||Computing token-dependent affective response baseline levels utilizing a database storing affective responses|
|US8955010||Jun 10, 2013||Feb 10, 2015||The Nielsen Company (Us), Llc||Methods and apparatus for providing personalized media in video|
|US8959541||May 29, 2012||Feb 17, 2015||Microsoft Technology Licensing, Llc||Determining a future portion of a currently presented media program|
|US8965822 *||Jun 25, 2011||Feb 24, 2015||Ari M. Frank||Discovering and classifying situations that influence affective response|
|US8977110||Aug 9, 2012||Mar 10, 2015||The Nielsen Company (Us), Llc||Methods and apparatus for providing video with embedded media|
|US8986218||Aug 12, 2013||Mar 24, 2015||Imotions A/S||System and method for calibrating and normalizing eye data in emotional testing|
|US8989835||Dec 27, 2012||Mar 24, 2015||The Nielsen Company (Us), Llc||Systems and methods to gather and analyze electroencephalographic data|
|US8990842 *||Feb 8, 2012||Mar 24, 2015||Disney Enterprises, Inc.||Presenting content and augmenting a broadcast|
|US9021515||Oct 24, 2012||Apr 28, 2015||The Nielsen Company (Us), Llc||Systems and methods to determine media effectiveness|
|US9031222 *||Aug 9, 2011||May 12, 2015||Cisco Technology, Inc.||Automatic supervisor intervention for calls in call center based upon video and/or speech analytics of calls|
|US9047256||Dec 30, 2009||Jun 2, 2015||Iheartmedia Management Services, Inc.||System and method for monitoring audience in response to signage|
|US9060671||Dec 27, 2012||Jun 23, 2015||The Nielsen Company (Us), Llc||Systems and methods to gather and analyze electroencephalographic data|
|US9076108 *||Jun 25, 2011||Jul 7, 2015||Ari M. Frank||Methods for discovering and classifying situations that influence affective response|
|US9100685 *||Dec 9, 2011||Aug 4, 2015||Microsoft Technology Licensing, Llc||Determining audience state or interest using passive sensor data|
|US9106958||Feb 27, 2012||Aug 11, 2015||Affectiva, Inc.||Video recommendation based on affect|
|US9154837||Dec 16, 2013||Oct 6, 2015||Microsoft Technology Licensing, Llc||User interface presenting an animated avatar performing a media reaction|
|US9179191 *||Dec 23, 2009||Nov 3, 2015||Sony Corporation||Information processing apparatus, information processing method, and program|
|US9183509 *||Jun 25, 2011||Nov 10, 2015||Ari M. Frank||Database of affective response and attention levels|
|US9204836||Oct 26, 2013||Dec 8, 2015||Affectiva, Inc.||Sporadic collection of mobile affect data|
|US9215978||Jan 30, 2015||Dec 22, 2015||The Nielsen Company (Us), Llc||Systems and methods to gather and analyze electroencephalographic data|
|US9230220 *||Jun 25, 2011||Jan 5, 2016||Ari M. Frank||Situation-dependent libraries of affective response|
|US9247903||Feb 6, 2012||Feb 2, 2016||Affectiva, Inc.||Using affect within a gaming context|
|US9265458||Dec 4, 2012||Feb 23, 2016||Sync-Think, Inc.||Application of smooth pursuit cognitive testing paradigms to clinical drug development|
|US9274598 *||Apr 29, 2004||Mar 1, 2016||International Business Machines Corporation||System and method for selecting and activating a target object using a combination of eye gaze and key presses|
|US9292858||Feb 27, 2012||Mar 22, 2016||The Nielsen Company (Us), Llc||Data collection system for aggregating biologically based measures in asynchronous geographically distributed public environments|
|US9295806||Mar 5, 2010||Mar 29, 2016||Imotions A/S||System and method for determining emotional response to olfactory stimuli|
|US9320450||Mar 14, 2013||Apr 26, 2016||The Nielsen Company (Us), Llc||Methods and apparatus to gather and analyze electroencephalographic data|
|US9336535||Feb 11, 2014||May 10, 2016||The Nielsen Company (Us), Llc||Neuro-response data synchronization|
|US9342576 *||Apr 4, 2014||May 17, 2016||Sony Corporation||Information processing device, information processing terminal, information processing method, and program|
|US9357240||Jan 21, 2009||May 31, 2016||The Nielsen Company (Us), Llc||Methods and apparatus for providing alternate media for video decoders|
|US9372544||May 16, 2014||Jun 21, 2016||Microsoft Technology Licensing, Llc||Gesture recognition techniques|
|US9373123||Jul 1, 2010||Jun 21, 2016||Iheartmedia Management Services, Inc.||Wearable advertising ratings methods and systems|
|US9380976||Mar 11, 2013||Jul 5, 2016||Sync-Think, Inc.||Optical neuroinformatics|
|US9400550||May 6, 2010||Jul 26, 2016||Nokia Technologies Oy||Apparatus and method providing viewer feedback of observed personal user data|
|US9412021 *||Nov 29, 2013||Aug 9, 2016||Nokia Technologies Oy||Method and apparatus for controlling transmission of data based on gaze interaction|
|US9418368||Dec 20, 2007||Aug 16, 2016||Invention Science Fund I, Llc||Methods and systems for determining interest in a cohort-linked avatar|
|US9442565 *||Aug 23, 2012||Sep 13, 2016||The United States Of America, As Represented By The Secretary Of The Navy||System and method for determining distracting features in a visual display|
|US9451303||Feb 27, 2013||Sep 20, 2016||The Nielsen Company (Us), Llc||Method and system for gathering and computing an audience's neurologically-based reactions in a distributed framework involving remote storage and computing|
|US9454646||Mar 31, 2014||Sep 27, 2016||The Nielsen Company (Us), Llc||Short imagery task (SIT) research method|
|US9495684||Apr 4, 2012||Nov 15, 2016||The Invention Science Fund I, Llc||Methods and systems for indicating behavior in a population cohort|
|US9503786||Aug 10, 2015||Nov 22, 2016||Affectiva, Inc.||Video recommendation using affect|
|US9513699||Oct 24, 2007||Dec 6, 2016||Invention Science Fund I, LL||Method of selecting a second content based on a user's reaction to a first content|
|US9521960||Oct 31, 2008||Dec 20, 2016||The Nielsen Company (Us), Llc||Systems and methods providing en mass collection and centralized processing of physiological responses from viewers|
|US9538219||Jan 26, 2012||Jan 3, 2017||Panasonic Intellectual Property Corporation Of America||Degree of interest estimating device and degree of interest estimating method|
|US9560984 *||Oct 29, 2009||Feb 7, 2017||The Nielsen Company (Us), Llc||Analysis of controlled and automatic attention for introduction of stimulus material|
|US9568997||Mar 25, 2014||Feb 14, 2017||Microsoft Technology Licensing, Llc||Eye tracking enabled smart closed captioning|
|US9569986||Feb 27, 2013||Feb 14, 2017||The Nielsen Company (Us), Llc||System and method for gathering and analyzing biometric user feedback for use in social media and advertising applications|
|US9571877||Mar 30, 2015||Feb 14, 2017||The Nielsen Company (Us), Llc||Systems and methods to determine media effectiveness|
|US9582805||Dec 11, 2007||Feb 28, 2017||Invention Science Fund I, Llc||Returning a personalized advertisement|
|US9606621 *||Jul 10, 2007||Mar 28, 2017||Philips Lighting Holding B.V.||Gaze interaction for information display of gazed items|
|US9622702||Jun 2, 2014||Apr 18, 2017||The Nielsen Company (Us), Llc||Methods and apparatus to gather and analyze electroencephalographic data|
|US9622703||Sep 21, 2015||Apr 18, 2017||The Nielsen Company (Us), Llc||Methods and apparatus to gather and analyze electroencephalographic data|
|US9628552||Oct 2, 2014||Apr 18, 2017||Google Inc.||Method and apparatus for digital media control rooms|
|US9628844||Jul 31, 2015||Apr 18, 2017||Microsoft Technology Licensing, Llc||Determining audience state or interest using passive sensor data|
|US9642536||Mar 15, 2014||May 9, 2017||Affectiva, Inc.||Mental state analysis using heart rate collection based on video imagery|
|US9646046||Mar 15, 2014||May 9, 2017||Affectiva, Inc.||Mental state data tagging for data collected from multiple sources|
|US9667513 *||Jan 24, 2013||May 30, 2017||Dw Associates, Llc||Real-time autonomous organization|
|US9668694||Mar 23, 2016||Jun 6, 2017||The Nielsen Company (Us), Llc||Methods and apparatus to gather and analyze electroencephalographic data|
|US9672535||Apr 24, 2016||Jun 6, 2017||Brian William Higgins||System and method for communicating information|
|US9704502 *||Jul 30, 2004||Jul 11, 2017||Invention Science Fund I, Llc||Cue-aware privacy filter for participants in persistent communications|
|US20030156304 *||Feb 19, 2002||Aug 21, 2003||Eastman Kodak Company||Method for providing affective information in an imaging system|
|US20050131697 *||Dec 10, 2003||Jun 16, 2005||International Business Machines Corporation||Speech improving apparatus, system and method|
|US20050131744 *||Dec 10, 2003||Jun 16, 2005||International Business Machines Corporation||Apparatus, system and method of automatically identifying participants at a videoconference who exhibit a particular expression|
|US20050222712 *||Dec 7, 2004||Oct 6, 2005||Honda Motor Co., Ltd.||Salesperson robot system|
|US20050243054 *||Apr 29, 2004||Nov 3, 2005||International Business Machines Corporation||System and method for selecting and activating a target object using a combination of eye gaze and key presses|
|US20060026626 *||Jul 30, 2004||Feb 2, 2006||Malamud Mark A||Cue-aware privacy filter for participants in persistent communications|
|US20060048189 *||Aug 2, 2005||Mar 2, 2006||Samsung Electronics Co., Ltd.||Method and apparatus for proactive recording and displaying of preferred television program by user's eye gaze|
|US20060093998 *||Sep 19, 2005||May 4, 2006||Roel Vertegaal||Method and apparatus for communication between humans and devices|
|US20060094934 *||Oct 19, 2005||May 4, 2006||Sony Corporation||Method and apparatus for processing bio-information|
|US20060224438 *||Jan 31, 2006||Oct 5, 2006||Hitachi, Ltd.||Method and device for providing information|
|US20070105071 *||Nov 6, 2006||May 10, 2007||Eye Tracking, Inc.||Generation of test stimuli in visual media|
|US20070265507 *||Mar 13, 2007||Nov 15, 2007||Imotions Emotion Technology Aps||Visual attention and emotional response detection and display system|
|US20080001951 *||May 7, 2007||Jan 3, 2008||Sony Computer Entertainment Inc.||System and method for providing affective characteristics to computer generated avatar during gameplay|
|US20080020361 *||Jul 12, 2007||Jan 24, 2008||Kron Frederick W||Computerized medical training system|
|US20080065468 *||Sep 7, 2007||Mar 13, 2008||Charles John Berg||Methods for Measuring Emotive Response and Selection Preference|
|US20080169930 *||Jan 17, 2007||Jul 17, 2008||Sony Computer Entertainment Inc.||Method and system for measuring a user's level of attention to content|
|US20090024964 *||Jul 16, 2007||Jan 22, 2009||Raj Gopal Kantamneni||Calculating cognitive efficiency score for navigational interfaces based on eye tracking data|
|US20090058660 *||Apr 1, 2005||Mar 5, 2009||Torch William C||Biosensors, communicators, and controllers monitoring eye movement and methods for using them|
|US20090113297 *||Oct 25, 2007||Apr 30, 2009||Searete Llc, A Limited Liability Corporation Of The State Of Delaware||Requesting a second content based on a user's reaction to a first content|
|US20090131764 *||Oct 31, 2008||May 21, 2009||Lee Hans C||Systems and Methods Providing En Mass Collection and Centralized Processing of Physiological Responses from Viewers|
|US20090146775 *||Sep 26, 2008||Jun 11, 2009||Fabrice Bonnaud||Method for determining user reaction with specific content of a displayed page|
|US20090172100 *||Dec 31, 2007||Jul 2, 2009||International Business Machines Corporation||Deriving and communicating attention spans in collaborative applications|
|US20090177528 *||May 4, 2007||Jul 9, 2009||National Ict Australia Limited||Electronic media system|
|US20090195392 *||Jan 31, 2008||Aug 6, 2009||Gary Zalewski||Laugh detector and system and method for tracking an emotional response to a media presentation|
|US20090285456 *||May 19, 2008||Nov 19, 2009||Hankyu Moon||Method and system for measuring human response to visual stimulus based on changes in facial expression|
|US20100007601 *||Jul 10, 2007||Jan 14, 2010||Koninklijke Philips Electronics N.V.||Gaze interaction for information display of gazed items|
|US20100010366 *||Dec 22, 2006||Jan 14, 2010||Richard Bernard Silberstein||Method to evaluate psychological responses to visual objects|
|US20100030097 *||Dec 22, 2006||Feb 4, 2010||Richard Bernard Silberstein||Method to determine the attributes associated with a brand or product|
|US20100056276 *||Dec 22, 2006||Mar 4, 2010||Neuroinsight Pty. Ltd.||Assessment of computer games|
|US20100092934 *||Dec 22, 2006||Apr 15, 2010||Richard Bernard Silberstein||method to determine the psychological impact of entertainment or individual presenters|
|US20100094702 *||Dec 22, 2006||Apr 15, 2010||Richard Bernard Silberstein||Method for evaluating the effectiveness of commercial communication|
|US20100169905 *||Dec 23, 2009||Jul 1, 2010||Masaki Fukuchi||Information processing apparatus, information processing method, and program|
|US20100174586 *||Mar 18, 2010||Jul 8, 2010||Berg Jr Charles John||Methods for Measuring Emotive Response and Selection Preference|
|US20100265354 *||Apr 19, 2010||Oct 21, 2010||Fujifilm Corporation||Image processing system, image processing method, and computer readable medium|
|US20100266213 *||Apr 16, 2010||Oct 21, 2010||Hill Daniel A||Method of assessing people's self-presentation and actions to evaluate personality type, behavioral tendencies, credibility, motivations and other insights through facial muscle activity and expressions|
|US20100278318 *||Apr 30, 2009||Nov 4, 2010||Avaya Inc.||System and Method for Detecting Emotions at Different Steps in a Communication|
|US20110038547 *||Aug 13, 2010||Feb 17, 2011||Hill Daniel A||Methods of facial coding scoring for optimally identifying consumers' responses to arrive at effective, incisive, actionable conclusions|
|US20110043617 *||Jul 26, 2010||Feb 24, 2011||Roel Vertegaal||Method and Apparatus for Communication Between Humans and Devices|
|US20110077548 *||Feb 9, 2009||Mar 31, 2011||Torch William C||Biosensors, communicators, and controllers monitoring eye movement and methods for using them|
|US20110105937 *||Oct 29, 2009||May 5, 2011||Neurofocus, Inc.||Analysis of controlled and automatic attention for introduction of stimulus material|
|US20110106589 *||Nov 3, 2009||May 5, 2011||James Blomberg||Data visualization platform for social and traditional media metrics analysis|
|US20110142413 *||Dec 3, 2010||Jun 16, 2011||Lg Electronics Inc.||Digital data reproducing apparatus and method for controlling the same|
|US20110161160 *||Dec 30, 2009||Jun 30, 2011||Clear Channel Management Services, Inc.||System and method for monitoring audience in response to signage|
|US20110211056 *||Mar 1, 2010||Sep 1, 2011||Eye-Com Corporation||Systems and methods for spatially controlled scene illumination|
|US20110261049 *||Jun 19, 2009||Oct 27, 2011||Business Intelligence Solutions Safe B.V.||Methods, apparatus and systems for data visualization and related applications|
|US20120046993 *||Apr 18, 2011||Feb 23, 2012||Hill Daniel A||Method and report assessing consumer reaction to a stimulus by matching eye position with facial coding|
|US20120191542 *||Jan 24, 2009||Jul 26, 2012||Nokia Corporation||Method, Apparatuses and Service for Searching|
|US20120204202 *||Feb 8, 2012||Aug 9, 2012||Rowley Marc W||Presenting content and augmenting a broadcast|
|US20120290401 *||May 11, 2011||Nov 15, 2012||Google Inc.||Gaze tracking system|
|US20120290511 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Database of affective response and attention levels|
|US20120290512 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Methods for creating a situation dependent library of affective response|
|US20120290513 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Habituation-compensated library of affective response|
|US20120290514 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Methods for predicting affective response from stimuli|
|US20120290515 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Affective response predictor trained on partial data|
|US20120290516 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Habituation-compensated predictor of affective response|
|US20120290520 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Affective response predictor for a stream of stimuli|
|US20120290521 *||Jun 25, 2011||Nov 15, 2012||Affectivon Ltd.||Discovering and classifying situations that influence affective response|
|US20130039483 *||Aug 9, 2011||Feb 14, 2013||Cisco Technology, Inc.||Automatic Supervisor Intervention for Calls in Call Center Based Upon Video and/or Speech Analytics of Calls|
|US20130050268 *||Aug 23, 2012||Feb 28, 2013||Maura C. Lohrenz||System and method for determining distracting features in a visual display|
|US20130102854 *||Dec 7, 2012||Apr 25, 2013||Affectiva, Inc.||Mental state evaluation learning for advertising|
|US20130151333 *||Dec 7, 2012||Jun 13, 2013||Affectiva, Inc.||Affect based evaluation of advertisement effectiveness|
|US20130204535 *||Feb 3, 2012||Aug 8, 2013||Microsoft Corporation||Visualizing predicted affective states over time|
|US20130238394 *||Apr 20, 2013||Sep 12, 2013||Affectiva, Inc.||Sales projections based on mental states|
|US20140058828 *||Oct 31, 2013||Feb 27, 2014||Affectiva, Inc.||Optimizing media based on mental state analysis|
|US20140304289 *||Apr 4, 2014||Oct 9, 2014||Sony Corporation||Information processing device, information processing terminal, information processing method, and program|
|US20150080675 *||Sep 12, 2014||Mar 19, 2015||Nhn Entertainment Corporation||Content evaluation system and content evaluation method using the system|
|US20150154445 *||Nov 29, 2013||Jun 4, 2015||Nokia Corporation||Method and apparatus for controlling transmission of data based on gaze interaction|
|US20160191995 *||Mar 4, 2016||Jun 30, 2016||Affectiva, Inc.||Image analysis for attendance query evaluation|
|USRE41376||Apr 3, 2007||Jun 15, 2010||Torch William C||System and method for monitoring eye movement|
|USRE42471||Aug 27, 2008||Jun 21, 2011||Torch William C||System and method for monitoring eye movement|
|CN101999108B||Jan 28, 2009||Aug 20, 2014||美国索尼电脑娱乐有限责任公司||Laugh detector and system and method for tracking an emotional response to a media presentation|
|CN102934458A *||Jan 26, 2012||Feb 13, 2013||松下电器产业株式会社||Interest estimation device and interest estimation method|
|CN102934458B *||Jan 26, 2012||Jun 29, 2016||松下电器（美国）知识产权公司||兴趣度估计装置以及兴趣度估计方法|
|CN103455580A *||Aug 26, 2013||Dec 18, 2013||华为技术有限公司||Information recommending method and information recommending device|
|EP2042969A1 *||Sep 28, 2007||Apr 1, 2009||Alcatel Lucent||Method for determining user reaction with specific content of a displayed page.|
|WO2008077177A1 *||Dec 22, 2006||Jul 3, 2008||Neuro-Insight Pty. Ltd.||A method to evaluate psychological responses to visual objects|
|WO2009040437A2 *||Sep 29, 2008||Apr 2, 2009||Alcatel Lucent||Method for determining user reaction with specific content of a displayed page|
|WO2009040437A3 *||Sep 29, 2008||Jun 4, 2009||Alcatel Lucent||Method for determining user reaction with specific content of a displayed page|
|WO2009097337A1 *||Jan 28, 2009||Aug 6, 2009||Sony Computer Entertainment America, Inc.||Laugh detector and system and method for tracking an emotional response to a media presentation|
|WO2012136599A1 *||Mar 30, 2012||Oct 11, 2012||Nviso Sa||Method and system for assessing and measuring emotional intensity to a stimulus|
|U.S. Classification||715/863, 715/831|
|Feb 25, 1999||AS||Assignment|
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DRYER, D. CHRISTOPHER;FLICKNER, MYRON DALE;MAO, JIANCHANG;REEL/FRAME:009800/0837;SIGNING DATES FROM 19990127 TO 19990208
|Nov 9, 2007||AS||Assignment|
Owner name: IPG HEALTHCARE 501 LIMITED, UNITED KINGDOM
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